Search Results for author:
Found 4 papers, 0 papers with code
Goodness-of-Fit of Attributed Probabilistic Graph Generative Models
Probabilistic generative models of graphs are important tools that enable representation and sampling.
Advanced Methods for Connectome-Based Predictive Modeling of Human Intelligence: A Novel Approach Based on Individual Differences in Cortical Topography
Investigating the proposed modeling framework's efficacy, we find that advanced connectome-based predictive modeling generates neuroscience predictions that account for a significantly greater proportion of variance in general intelligence scores than previously established methods, advancing our scientific understanding of the network architecture that underlies human intelligence.
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being
In this paper, we create a benchmark for predictive analysis of individuals from a perspective that integrates: physical and physiological behavior, psychological states and traits, and job performance.
Using Bayesian Network Representations for Effective Sampling from Generative Network Models
Bayesian networks (BNs) are used for inference and sampling by exploiting conditional independence among random variables.
